The last time, we introduced you to the fundamental building blocks of a weather model, and demonstrated how important it was to have a high-resolution, fine-grained weather model in order to be able to project underlying topography.
Another important aspect is that the quality of a weather model is highly dependent on what often gets referred to as “ground truth” .
Ground truth describes actual measurements and observations of weather stations that are used for initialization of the weather model, so it “knows” where to start from. Besides ground weather stations, other measurement methods like weather balloon soundings, radars or satellites are used to complement the initialization data set.
The more measured data the weather model has from its point of initialization, the better the forecast will be as there will be less inaccuracy to begin with. This is especially true for the first forecast hours of very high resolution models because they are able to reflect small-scale features. If the starting point of the model is already a poor guess, any forecast resulting from it will be likely even worse than that.
The density of weather observations across the world is very diverse with some countries having hundreds of weather stations and countries with regions that are almost unknown regarding measured meteorological parameters.
Cross-CPP can help with that, as it aims to provide a platform where service providers like us can buy weather data from sensors, that origin from cars, buildings or other technology. These sensors need a different handling than regular weather station data and must undergo a special plausibility check, that we develop within the project. However, because of their density, their data will still help with the model initialization process, especially in regions and areas where “ground truth” is currently rare.
Individualized weather forecasts
Another aspect of access to different data sources is that we are able to enhance our services for the data provider themselves. For example, imagine a building owners who want to automate the operation of window blinds or optimize energy usage (heating, cooling, etc.): these owners would benefit greatly from a tailored weather forecast for their buildings, that takes the special local meteorological characteristics into account and adjusts for them. Smart buildings usually own a weather station located on the roof of a building, whose data we can use to refine our forecast for that specific building after having collected at least ~1 year of measurements from this station.
Weather sensors and weather data can be derived from various sources and transformed into a variety of use cases. In our next blog, we will show you some other products we are working on like weather-based navigation and a car sensor derived precipitation map!
Thanks for reading and stay with us 🙂
Your Meteologix Team and Cross-CPP consortium partners
The Cross-CPP (Cross-Cyber Physical Products) Project and its consortium partners aim to build a cross-sectorial marketplace, that offers data from various sources.
Service providers can then use this data to enhance their services and offer them for example back to the data owner, e.g. if you are driving a car and opt to share the outside temperature data of it.
We Meteologix as a meteorological service provider can use this data to enhance our own “SwissHD” forecast-system, and in turn provide you with a tailored and even better weather forecast for your car and travel.
To understand this whole process, it might be helpful to dig a little bit into the theory, how modern weather forecast is done in the first place.
Modern forecast-systems are highly complex computer programs, that consist of thousands of lines of code needed to compute a forecast for a specific location at a certain point in time with the help of algorithms,
that process vast amounts of data for these grid points around the world.
What’s a grid point then?
Imagine laying a mesh around the globe – then each node within this mesh is a grid point.
For each grid point a forecast is calculated, that takes the height and other geographical features of this specific location into account. Of course, you can also get a forecast for any other location that is not a grid point: this is achieved by interpolation between nearby grid points.
Thus, the farther away the grid points are from each other and the more coarse-meshed a weather model is, the poorer is its resolution and the more interpolation is needed and vice versa.
There are a lot of weather models on the market and they differ tremendously in resolution, the probably most famous and widely used Global-forecast-system (GFS) has a grid point only every ~22km in mid-latitudes. The use of its data is free, which is why it is the basis of a lot of (low quality) weather apps.
You can observe the problems that arise from low resolution easily in the following comparison of pictures of the terrain in Liechtenstein, that each model can “see” and differentiate with their grid point densities.
Let’s take a look how well these different model resolutions reflect the topography of Liechtenstein:
The first one is a model with grid points every 22km, then one with grid points every 13km, then a ~7km grid, and the last one is our Meteologix Swiss HD 1km model. The differences are quite obvious: the coarse-meshed models only capture two to four different terrain heights as they get averaged and smoothed out. Meaning these models do only take these few different regional features into account, when computing their forecast, which leads to very biased weather predictions. The two more fine-grained models differentiate the regional ground features much better.
Of course there is more to a weather model than just the density of the grid points, its inner logic and formulas are very important as well, but if the mesh is too broad, the underlying topography cannot be projected realistically. The same applies to forecasts of small-scale weather events, such as showers and thunderstorms where higher-resolution models can predict their evolution more accurately than coarser models. Thus, all mathematical sophistication does not help, when the weather model does not “know” for what kind of terrain it calculates the forecast for.
Hence, it is important to have a high-grained weather model to begin with in order to make reasonable forecasts, although it is also important to have as much “ground truth” as possible to enhance the model’s forecasts.
What exactly is meant by “ground truth” and how this Cross-CPP project aims to help with that, so that you as a consumer can get the best weather predictions as possible, we will explain in our next weather blog post.
Stay tuned 🙂
Your Meteologix Team and Cross-CPP consortium partners